#production

How AI Execution Lab runs autonomously on free/hobby plans: model-tier routing, content-addressed caching, semantic deduplication, publish throttling, empty-queue early-exit crons, Firestore read/write minimization via increment counters, and batched embeddings. Includes expensive-operation analysis, scaling bottlenecks, the cheapest viable architecture, and estimated monthly cost ranges.

How AI Execution Lab uses Vertex AI Gemini 2.5 (Flash + Pro) exclusively: dependency-free service-account auth, model-tier routing with automatic fallback, graceful rate-limit handling, token usage + cost tracking, Vertex quota monitoring, batched multilingual embeddings, and a deterministic mock fallback. Includes the exact env vars, IAM roles, and a go-live checklist.

Operational cost governance doctrine for TrustSeal and ScamCheck. Documents where costs originate, concrete free-tier economics, the 7 cost invariants that prevent runaway resource consumption, scaling thresholds with upgrade triggers, abuse containment strategy, and silent cost escalation vectors. All figures derived from real architecture — Gemini 1.5-flash free tier, Firebase Spark plan, Razorpay transaction fees.

Platform-specific deployment verification checklists for Vercel (AI Execution Lab), Firebase (TrustSeal and ScamCheck Cloud Functions), GitHub Pages (TrustSeal and ScamCheck SPAs), and WordPress (asquaresolution.com). A deploy is not safe until every item on the relevant checklist has been confirmed in production — not in the emulator, not locally, not from build logs.

Recovery invariants, incident classification, blast radius model, and recovery posture for the A Square Solutions ecosystem. Extracted from real production incidents across TrustSeal, ScamCheck, AI Execution Lab, and WordPress. Answers the question: when production behavior diverges from expected state, how do we restore safe operation predictably and without making the incident worse?

The 20 operational invariants governing the A Square Solutions ecosystem, extracted from real production failures and operational history. Each invariant is a condition that must remain true for the system to behave safely and predictably — an explicit reliability contract derived from TrustSeal, ScamCheck, AI Execution Lab, and WordPress production experience.

Orientation for new operators, contributors, and AI sessions entering the A Square Solutions ecosystem. Covers the three-product architecture, platform independence model, doctrine navigation map, safe contribution zones, the ten most operationally critical facts, and a glossary of platform-specific behaviors. Start here before making any production changes.

Security invariants, credential governance, trust boundary model, and access discipline for the A Square Solutions ecosystem. Documents the three-tier access architecture across TrustSeal and ScamCheck, all credentials and where they are allowed, the security implications of historical operational failures, silent security drift scenarios, and lightweight security observability patterns. Grounded entirely in real production architecture.

How operators make sound decisions during deployments, failures, recovery, and production uncertainty. Ten operator invariants extracted from real incidents where assumption, pressure, and incomplete verification made incidents worse or masked them for weeks. Answers the question: how do humans avoid making production incidents worse under pressure?

Detection invariants, signal taxonomy, and monitoring doctrine for the A Square Solutions ecosystem. Extracted from real production failure history across TrustSeal, ScamCheck, AI Execution Lab, and WordPress. Documents how 15 historical failures were detected, what signals were missing, and what detection rules prevent the same classes from being discovered by user reports instead of operators.

How changes move safely from intent to stable production operation. Change classification framework, blast radius evaluation, preflight discipline, staging philosophy, and change-management invariants extracted from real deployment history across TrustSeal, ScamCheck, AI Execution Lab, and WordPress. Answers: how do we reduce the probability that a production change introduces unexpected operational behavior?

Step-by-step detection procedures for every production system in the A Square Solutions ecosystem. Covers TrustSeal, ScamCheck, AI Execution Lab, and WordPress. For each system: what healthy looks like, what each failure mode looks like, and what to check first when something is wrong.

Lightweight, system-specific recovery procedures for every documented failure class across the A Square Solutions ecosystem. For each failure: the minimum recovery action, the correct recovery sequence, how to confirm the system is restored, and what residual risk remains. Companion to the Incident Detection Playbook.

Operational pattern for handling structured output from AI APIs (Gemini, GPT, Claude) in production. Covers the failure surface when AI output is used as data: JSON parse failures, schema drift, missing fields, type mismatches, markdown code fence wrapping, and the architectural patterns that make AI-driven data pipelines robust against model output variation.

Production pattern for per-user quota tracking, monthly reset logic, atomic increment, pre-AI-call enforcement, and abuse prevention using Firestore. Implemented in TrustSeal (10 free checks/month, premium tier) and ScamCheck (unlimited free after sign-up). Covers the data model, the enforcement code, the reset mechanism, and the cost protection logic that prevents free-tier Gemini quota from being exhausted by a single user.

Operational reference for running Gemini AI in production via Firebase Cloud Functions. Covers: structured output enforcement, JSON parse failure handling, 429 rate limit UX design, server-side key isolation, cold start latency mitigation, Node runtime requirements, and the three-part prompt architecture that produces reliable structured output across calls.

Firebase Cloud Functions returned 403 errors with missing auth context for 12 minutes after a redeploy that included a Firestore rules update. Root cause: Functions were deployed before Rules, creating a window where new function code ran against stale IAM/rules state. Fix: always deploy Firestore rules before Cloud Functions when both change in the same release.

How A Square Solutions builds production AI systems: the production-first philosophy, failure indexing methodology, evidence-backed documentation practice, and Claude Code operational workflow. Not a methodology document — an engineering record.

What it actually takes to operate a Next.js 15 App Router platform on Vercel in production: deployment configuration, monitoring, known failure modes, build performance, and the operational discipline that keeps it stable. From real operational experience on AI Execution Lab.

Complete guide for deploying AI Execution Lab to lab.asquaresolution.com — DNS configuration, Vercel setup, environment variables, SSL, and launch verification.

A new Gemini API key environment variable was added to .env.local but not to Vercel production. Feature worked locally and in dev but silently returned undefined in production. Root cause: variable scoped to Development only.